Creditor institutions often rely on creditworthiness scores when determining whether to accept or deny an individual's application for a credit product, such as a credit card or line of credit. Some creditor institutions buy creditworthiness scores, such as FICO scores, from consumer reporting agencies (“CRAs”). CRAs collect personal and financial information about individual consumers, generate a credit report to indicate the creditworthiness of individual consumers, and sell these credit reports to prospective creditors. More specifically, CRAs collect personal and financial information about individual consumers from a variety of sources called data furnishers. These data furnishers are typically institutions that have had financial relationships with individual consumers. For example, data furnishers may be creditors, lenders, utility companies, debt collection agencies, government agencies, and courts. Data furnishers report data regarding individual consumers to CRAs, and, based on the received data, CRAs generate a credit report for each individual consumer. A typical credit report contains detailed information about an individual consumer's credit history, including credit accounts and loans, proceedings for a person unable to repay outstanding financial obligations, late payments, and recent inquiries. The CRAs then calculate the creditworthiness score using the information provided in the credit report. Some creditor institutions develop their own creditworthiness scores based on their own past experiences with individuals. Oftentimes, these creditor institutions combine their own creditworthiness scores with those purchased from CRAs to create a blended creditworthiness score.
Before a creditor institution can use a creditworthiness score to make a credit-approval determination for an individual credit applicant, the creditor institution must first establish credit-approval thresholds. For example, for each credit product offered, the creditor institution must determine a range of creditworthiness scores that would qualify an individual for that credit product. Credit products that have higher lines of credit will require higher qualifying creditworthiness scores than those with lower lines of credit.
To establish credit-approval thresholds, creditor institutions sometimes develop decision models around historical-performance data taken from individuals having varying creditworthiness scores. A creditor institution may build a decision model around historical-performance data taken from individuals that have varying credit worthiness scores and, for example, were booked for a particular credit product sometime between January 2003 and December 2004. In this case, the creditor institution would build the decision model to group individuals by creditworthiness scores and calculate the average failure to repay rate for each group. In this case, the creditor institution could input a particular credit applicant's creditworthiness score, and the model would output the average failure to repay rate for the group to which that individual belongs. The outputted failure to repay rate represents the likelihood that the particular credit applicant will fail to repay. If the outputted failure to repay rate is acceptable to the creditor institution, then the institution approves the particular credit-applicant's credit application, provided the particular credit-applicant meets all other requirements.
Instead of using the decision model to approve or deny individual applicants' credit applications on a case-by-case basis, some creditor institutions use decision models to establish credit-approval thresholds. For example, a creditor institution can determine an acceptable rate of failure to repay for a particular credit product, and then refer to the risk model to identify the creditworthiness score associated with the determined acceptable rate of failure to repay. This creditworthiness score becomes the credit-approval threshold for that particular product. Accordingly, when making credit-approval determinations, the creditor institution approves applicants having creditworthiness scores equal to or higher than the credit-approval threshold and rejects those having creditworthiness scores lower than the credit-approval threshold.
However, because the exemplary decision model described above was built using historical-performance data taken from individuals that were booked sometime between January 2003 and December 2004, assumptions about the economic environment of that time are built into the model. Accordingly, the decision model is static and unable to account for fluctuations in the business cycle and other economic conditions that affect borrowers' ability to repay debt. This exemplary decision model would under-predict risk if used to make approval decisions or establish credit-approval thresholds in 2008, because the economic environment in 2008 was less stable than the economic environment around which the decision model was built. Under predicting risk would lead to actual failure to repay rates that are higher than the failure to repay rates predicted by the decision model. On the other hand, this exemplary decision model would over-predict risk if used to make approval decisions or establish credit-approval thresholds when the economic environment is more stable than the economic environment around which the decision model was built. Over predicting risk would lead to unnecessary credit restrictions.
Even if the exemplary decision model were frequently updated with the most recent historical-performance data, the decision model would still be using historical data to predict future failure to repay rates. Accordingly, the decision model will always lag fluctuations in the business cycle, thereby resulting in credit-approval thresholds that are adjusted in response to, instead of in anticipation of, fluctuations in the business cycle.
There is a need for systems, devices, methods, and other tools that provide decision models incorporated with macroeconomic variables that enable the decision models to anticipate fluctuations in the business cycle.